A multi-weapon detection using ensembled learning

Abstract

Recently, the level of criminals and terrorists using light weapons (such as knives and firearms) has increased rapidly around the world. Unfortunately, most current surveillance systems are still based mainly on human monitoring and intervention. For that reason, the requirement for a smart system for detecting different weapons becomes crucial in the field of security and computer vision. In this article, a novel technique for detecting various types of weapons has been proposed. This system is based mainly on deep learning techniques, namely, You Only Look Once, version 8 (YOLOv8), to detect a different class of light weapons. Furthermore, this study focuses on detecting two armed human poses based on ensemble learning techniques, which involve combining the outputs of different Yolov8 models to produce an accurate and robust detection system. The proposed system is evaluated on the self-created weapons dataset comprising thousands of images of different classes of weapons. The experiment results of this work show the effectiveness of ensemble learning for detecting various weapons with high accuracy, achieving 97.2% of mean average precision.

Keywords

• artificial intelligence
• computer vision
• object detection
• convolutional neural networks
• you only look once